Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under formal
-
, Germany. We Offer You: A World-Class Environment: Access to a leading research environment specializing in hardware/software for medical wearables, translational endocrinology, and machine learning
-
complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
-
will utilize economic theory, simulation, economic evaluation and machine learning to quantify the benefits of advanced diagnostic technologies in reducing overdiagnosis. Competence You must have
-
embedded in a multidisciplinary research environment combining expertise in machine learning (ML), numerical modelling, satellite remote sensing, and Arctic geosciences. The Centre is actively involved in
-
, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in
-
broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
-
and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal clubs, and research
-
is linked to the new research center FME RenewHydro . You will join the research group Electrical Machines and Electromagnetics (EME) at IEL, where we foster an open, inclusive, and collaborative
-
Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning, neural networks, prompt engineering, human